The quality of student reflective responses has been shown to positively correlate with student learning gains. However, providing feedback on reflection quality to students is typically expensive and delayed. In this work, we automatically predict the quality of student reflective responses using natural language processing. With the long-term goal of producing informative feedback for students, we derive a new set of predictive features from a human quality-coding rubric. An off-line intrinsic evaluation demonstrates the effectiveness of the proposed features in predicting reflection quality, particularly when training and testing on different lectures, topics, and courses. An extrinsic evaluation shows that both expert-coded quality ratings and quality predictions based on the new features positively correlate with student learning gain.